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Genome Modeling System: A Knowledge Management Platform for Genomics

2015; Public Library of Science; Volume: 11; Issue: 7 Linguagem: Inglês

10.1371/journal.pcbi.1004274

1553-7358

Malachi Griffith, Obi L. Griffith, Scott M. Smith, Avinash Ramu, Matthew B. Callaway, Anthony M. Brummett, Michael J. Kiwala, Adam Coffman, Allison Regier, Ben J. Oberkfell, Gabriel E. Sanderson, Thomas P. Mooney, Nathaniel G. Nutter, Edward A. Belter, Feiyu Du, Robert L. Long, Travis E. Abbott, Ian Ferguson, David Morton, Mark M. Burnett, James V. Weible, Joshua B. Peck, Adam F. Dukes, Joshua F. McMichael, Justin T. Lolofie, Brian R. Derickson, Jasreet Hundal, Zachary L. Skidmore, Benjamin J. Ainscough, Nathan D. Dees, William Schierding, Cyriac Kandoth, Kyung H. Kim, Charles Lu, Christopher Harris, Nicole Maher, Christopher A. Maher, Vincent Magrini, Benjamin Abbott, Ken Chen, Eric M. Clark, Indraniel Das, Xian Fan, Amy Hawkins, Todd G. Hepler, Todd Wylie, Shawn Leonard, W.E. Schroeder, Xiaoqi Shi, Lynn K. Carmichael, Matthew R. Weil, Richard W. Wohlstadter, Gary Stiehr, Michael D. McLellan, Craig Pohl, Christopher A. Miller, Daniel C. Koboldt, Jason Walker, James M. Eldred, David E. Larson, David J. Dooling, Li Ding, Elaine R. Mardis, Richard K. Wilson,

Cancer Genomics and Diagnostics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.